Predicting Gene-Disease Associations Using Multiple Species Data

Natarajan, Nagarajan; Blom, U. Martin; Tewari, Ambuj; Woods, John O.; Dhillon, Inderjit S.; Marcotte, Edward M.

doi:10.21236/ada585568

Cited by 6 publications

(8 citation statements)

References 25 publications

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“…A low rank model induces significant correlation between the predictions of different tasks. This assumption matches observations made by domain experts that the gene ranking profiles of diseases are exhibit strong correlations [18], [22]. This assumption is further validated by the empirical performance of the low rank model.…”

Section: Introductionsupporting

confidence: 82%

See 1 more Smart Citation

The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking

Koyejo¹,

Liu²,

Ghosh³

2013

Preprint

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We propose a novel hierarchical model for multitask bipartite ranking. The proposed approach combines a matrix-variate Gaussian process with a generative model for task-wise bipartite ranking. In addition, we employ a novel trace constrained variational inference approach to impose low rank structure on the posterior matrix-variate Gaussian process. The resulting posterior covariance function is derived in closed form, and the posterior mean function is the solution to a matrix-variate regression with a novel spectral elastic net regularizer. Further, we show that variational inference for the trace constrained matrix-variate Gaussian process combined with maximum likelihood parameter estimation for the bipartite ranking model is jointly convex. Our motivating application is the prioritization of candidate disease genes. The goal of this task is to aid the identification of unobserved associations between human genes and diseases using a small set of observed associations as well as kernels induced by genegene interaction networks and disease ontologies. Our experimental results illustrate the performance of the proposed model on real world datasets. Moreover, we find that the resulting low rank solution improves the computational scalability of training and testing as compared to baseline models.

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Section: Introductionsupporting

confidence: 82%

“…The disease-gene prioritization task has received a significant amount of study in recent years [15], [16], [17], [18]. The task is challenging because all the observed responses correspond to known associations and the states of the unobserved associations are unknown, i.e., there are no reliable negative examples.…”

Section: Introductionmentioning

confidence: 99%

The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking

Koyejo¹,

Liu²,

Ghosh³

2013

Preprint

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“…Assessing the magnitude of this decrease and the robustness to noise of different learning paradigms has been subject of intense investigation in the machine learning community. 17,[19][20][21][22][23][24][25] Most of these studies have dealt with classification problems. 19,20,22,26 Nettleton et al 26 compared the tolerance to noise, both on the descriptors and on the class labels, of the following classifiers on 13 highly unbalanced data sets: (i) Naive Bayes, 27 (ii) C4.5 decision trees, 28 (iii) IBk instance-based learner, 29 and (iv) Sequential Minimal Optimization (SMO) Support Vector Machines (SVM).…”

Section: Introductionmentioning

confidence: 99%

Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets

Cortés-Ciriano

Bender

Malliavin

2015

J. Chem. Inf. Model.

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To date, no systematic study has assessed the effect of random experimental errors on the predictive power of QSAR models. To address this shortage, we have benchmarked the noise sensitivity of 12 learning algorithms on 12 data sets (15,840 models in total), namely the following: Support Vector Machines (SVM) with radial and polynomial (Poly) kernels, Gaussian Process (GP) with radial and polynomial kernels, Relevant Vector Machines (radial kernel), Random Forest (RF), Gradient Boosting Machines (GBM), Bagged Regression Trees, Partial Least Squares, and k-Nearest Neighbors. Model performance on the test set was used as a proxy to monitor the relative noise sensitivity of these algorithms as a function of the level of simulated noise added to the bioactivities from the training set. The noise was simulated by sampling from Gaussian distributions with increasingly larger variances, which ranged from zero to the range of pIC50 values comprised in a given data set. General trends were identified by designing a full-factorial experiment, which was analyzed with a normal linear model. Overall, GBM displayed low noise tolerance, although its performance was comparable to RF, SVM Radial, SVM Poly, GP Poly, and GP Radial at low noise levels. Of practical relevance, we show that the bag fraction parameter has a marked influence on the noise sensitivity of GBM, suggesting that low values (e.g., 0.1-0.2) for this parameter should be set when modeling noisy data. The remaining 11 algorithms display a comparable noise tolerance, as a smooth and linear degradation of model performance is observed with the level of noise. However, SVM Poly and GP Poly display significant noise sensitivity at high noise levels in some cases. Overall, these results provide a practical guide to make informed decisions about which algorithm and parameter values to use according to the noise level present in the data.

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“…Liu and Tao [13], Scott, Blanchard and Handy [25], and Blanchard et al [1] study the consistency of the classifier under corruption, while Reeve et al [23] focus on the minimax optimal learning rate of the corrupted estimator. Some recent works try correction of the loss function or the observed labels; see Natarajan et al [19], van Rooyen and Williamson [27], Patrini et al [21], and Lin and Bradic [12]. Other recent works focus on studying or developing label noise-robust methods; see Natarajan et al [18], Patrini et al [20], Reeve and Kabán [24], Bootkrajang and Kabán [3], and Bootkrajang and Kabán [4].…”

Section: Prior Workmentioning

confidence: 99%

Binary classification with corrupted labels

Lee

Barber

2022

Electron. J. Statist.

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In a binary classification problem where the goal is to fit an accurate predictor, the presence of corrupted labels in the training data set may create an additional challenge. However, in settings where likelihood maximization is poorly behaved-for example, if positive and negative labels are perfectly separable-then a small fraction of corrupted labels can improve performance by ensuring robustness. In this work, we establish that in such settings, corruption acts as a form of regularization, and we compute precise upper bounds on estimation error in the presence of corruptions. Our results suggest that the presence of corrupted data points is beneficial only up to a small fraction of the total sample, scaling with the square root of the sample size. MSC2020 subject classifications: Primary 62H30.

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Predicting Gene-Disease Associations Using Multiple Species Data

Cited by 6 publications

References 25 publications

The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking

The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking

Comparing the Influence of Simulated Experimental Errors on 12 Machine Learning Algorithms in Bioactivity Modeling Using 12 Diverse Data Sets

Binary classification with corrupted labels

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